Driver activity monitoring through supervised and unsupervised learning

Harini Veeraraghavan, Stefan Atev, Nathaniel Bird, Paul R Schrater, Nikolaos P Papanikolopoulos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Scopus citations


This paper presents two different learning methods applied to the task of driver activity monitoring. The goal of the methods is to detect periods of driver activity that are not safe, such as talking on a cellular telephone, eating, or adjusting the dashboard radio system. The system presented here uses a side-mounted camera looking at a driver's profile and utilizes the silhouette appearance obtained from skin-color segmentation for detecting the activities. The unsupervised method uses agglomerative clustering to succinctly represent driver activities throughout a sequence, while the supervised learning method uses a Bayesian eigen-image classifier to distinguish between activities. The results of the two learning methods applied to driving sequences on three different subjects are presented and extensively discussed.

Original languageEnglish (US)
Title of host publicationITSC`05
Subtitle of host publication2005 IEEE Intelligent Conference on Transportation Systems, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)0780392159, 9780780392151
StatePublished - 2005
Event8th International IEEE Conference on Intelligent Transportation Systems - Vienna, Austria
Duration: Sep 13 2005Sep 16 2005

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC


Other8th International IEEE Conference on Intelligent Transportation Systems


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